This framework has been renamed to ThingFlow.
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Ant Events


This framework has been renamed to ThingFlow. The latest repo is at Please go there.


Ant Events is a (Python3) framework for building IOT event processing dataflows. The goal of this framework is to support the creation of robust IoT systems from reusable components. These systems must account for noisy/missing sensor data, distributed computation, and the need for local (near the data source) processing.

The fundamental abstractions in Ant Events are event streams, which are push-based sequences of sensor data readings, and elements, which are reusable components to generate, transform, or consume the events on these streams. Elements can have simple, stateless logic (e.g. filter events based on a predicate) or implement more complex, stateful algorithms, such as Kalman filters or machine learning. Ant Events integrates with standard Python data analytics frameworks, including NumPy, Pandas, and scikit-learn. This allows dataflows involving complex elements to be developed and refined offline and then deployed in an IoT environment using the same code base.

Ant Events primarily uses an event-driven programming model, building on Python's asyncio module. In addition to being a natural programming model for realtime sensor data, it reduces the potential resource consumption of Ant Events programs. The details of event scheduling are handled by the framework. Separate threads may be used on the "edges" of a dataflow, where elements frequently interact with external components that have blocking APIs.


To give the flavor of Ant Events, below is a short code snippet for the Raspberry Pi that reads a light sensor and then turns on an LED if the running average of the last five readings is greater than some threshold:

lux = LuxSensor() e: e.val).running_avg(5)\
   .select(lambda v: v > THRESHOLD).GpioPinOut()

The first line creates an element representing sensor object and the second line creates a pipeline of elements to process the data from the element. The select element extracts the data value from the sensor event, the running_avg element averages the values, the next select element converts the value to a a boolean based on the threshold, and the GpioPinOut element turns on the LED based on the value of the boolean.

Getting Started


Ant Events does not have any required external dependendencies, so, in theory at least, it can be run just about anywhere you can run Python 3. It has been tested on the Raspberry Pi (Rasbian distribution), Desktop Linux, and MacOSX. In a desktop environment, you might find the Anaconda Python distribution helpful, as it comes with many data analytics tools (e.g. Jupyter, NumPy, Pandas, and scikit-learn) pre-installed.

Ant Events has been ported to Micropython, so that it can run on very small devices, like the ESP8266. Since these devices have stringent memory requirements, the code base has been stripped down to a core for the Micropython port. The port is in this repository under the micropython directory.

Installing Ant Events

We recommend installing into a virtualenv rather than directly into the system's Python. To install, first run the activate script of your chosen virtual environment, and go to the antevents-python directory. Then run:

python3 install

In the future, we will have support for installing from the Python Package Index, PyPi.

You can also run the Ant Events code in-place from the git repository by adding the full path to the antevents-python directory to your PYTHONPATH. This is how the tests and the examples are run.

Directory Layout

The layout of the files in the Ant Events code repository (the antevents-python directory) is as follows:

  • README.RST - this file, top level documentation
  • Makefile - builds the source distribution and documentation; can run the tests
  • - used to install the core code into a python environment
  • antevents/ - the core code. This is all that will get installed in a production system
    • antevents/ - the core definitions and base classes of antevents
    • antevents/adapters - reader and writer elements that talk to the outside world
    • antevents/linq - elements for filter pipelines, in the style of Microsoft's Linq framework
  • docs/ - documentation, in restructured text (.rst) format.
  • tests/ - the tests. These can be run in-place.
  • examples/ - examples and other documentation.
    • examples/notebooks - examples that use Jupyter
  • micropython/ - port of Ant Events core to Micropython

Next Steps

To learn more about AntEvents, look at the docs/ subdirectory. In particular, tutorial.rst is a good starting point.

There is a separate repository with larger AntEvents examples. It is at

Related Work

The architecture was heavily influenced by Microsoft's Rx (Reactive Extensions) framework and the Click modular router. We started by trying to simplfy Rx for the IoT case and remove some of the .NETisms. A key addition was the support for multiple topics, which makes more complex dataflows possible.